流处理系统的不确定性感知弹性虚拟机调度

Shigeru Imai, S. Patterson, Carlos A. Varela
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引用次数: 18

摘要

部署在云上的流处理系统需要具有弹性,以有效地适应工作负载随时间的变化。性能模型可以预测最大可持续吞吐量(MST)作为分配的虚拟机数量的函数。我们提出了一个调度框架,该框架结合了三种统计技术来提高云流处理系统的服务质量(QoS):(i)不确定性量化,以考虑MST模型中的方差;(ii)在线学习,在收集到新的绩效指标时更新MST模型;(iii)工作负载模型,用于预测输入数据流速率,假设随时间发生规律模式。我们的框架可以通过QoS满意度目标进行参数化,该目标可以在统计上找到最佳的性能/成本权衡。我们的研究结果表明,这三种技术中的每一种都能显著提高QoS,在8个基准应用程序中,QoS满意率平均从52%提高到73-81%。此外,应用所有三种技术使我们能够达到98.62%的QoS满意度,而成本不到最佳VM分配成本的两倍,并且为工作负载中的峰值需求分配VM的成本降低了一半。
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Uncertainty-Aware Elastic Virtual Machine Scheduling for Stream Processing Systems
Stream processing systems deployed on the cloud need to be elastic to effectively accommodate workload variations over time. Performance models can predict maximum sustainable throughput (MST) as a function of the number of VMs allocated. We present a scheduling framework that incorporates three statistical techniques to improve Quality of Service (QoS) of cloud stream processing systems: (i) uncertainty quantification to consider variance in the MST model; (ii) online learning to update MST model as new performance metrics are gathered; and (iii) workload models to predict input data stream rates assuming regular patterns occur over time. Our framework can be parameterized by a QoS satisfaction target that statistically finds the best performance/cost tradeoff. Our results illustrate that each of the three techniques alone significantly improves QoS, from 52% to 73-81% QoS satisfaction rates on average for eight benchmark applications. Furthermore, applying all three techniques allows us to reach 98.62% QoS satisfaction rate with a cost less than twice the cost of the optimal (in hindsight) VM allocations, and half of the cost of allocating VMs for the peak demand in the workload.
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